A generalizable data assembly algorithm for infectious disease outbreaks.

ABSTRACT: During infectious disease outbreaks, health agencies often share text-based information about cases and deaths. This information is rarely machine-readable, thus creating challenges for outbreak researchers. Here, we introduce a generalizable data assembly algorithm that automatically curates text-based, outbreak-related information and demonstrate its performance across 3 outbreaks. After developing an algorithm with regular expressions, we automatically curated data from health agencies via 3 information sources: formal reports, email newsletters, and Twitter. A validation data set was also curated manually for each outbreak, and an implementation process was presented for application to future outbreaks. When compared against the validation data sets, the overall cumulative missingness and misidentification of the algorithmically curated data were ≤2% and ≤1%, respectively, for all 3 outbreaks. Within the context of outbreak research, our work successfully addresses the need for generalizable tools that can transform text-based information into machine-readable data across varied information sources and infectious diseases.

SUBMITTER: Majumder MS 

PROVIDER: S-EPMC8327373 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature